In image recognition, an over-complete dictionary as a collection of bases can be utilized to represent and reconstruct images. The dictionary can be optimized to include a large set of bases, but typically uses only a small group of bases to reconstruct a particular image. Therefore, the representation is generally sparse. To classify an image, it is desirable for the dictionary to contain discriminative features from different classes so that the sparse representation would indicate class labels.
However, the image can typically comprise both generic features and class-specific features. Therefore, for an image recognition system that is based on a recognition by reconstruction scheme, it is desirable that the dictionary possesses not only discriminative power for classification purpose, but also reconstructive power for error tolerance.